Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations1060708
Missing cells32061
Missing cells (%)0.1%
Duplicate rows88323
Duplicate rows (%)8.3%
Total size in memory536.6 MiB
Average record size in memory530.4 B

Variable types

Numeric14
DateTime3
Categorical2
Text2

Alerts

Dataset has 88323 (8.3%) duplicate rowsDuplicates
Breadth is highly overall correlated with Draught and 1 other fieldsHigh correlation
COG is highly overall correlated with THHigh correlation
Draught is highly overall correlated with Breadth and 3 other fieldsHigh correlation
EndLatitude is highly overall correlated with EndLongitude and 6 other fieldsHigh correlation
EndLongitude is highly overall correlated with EndLatitude and 6 other fieldsHigh correlation
EndPort is highly overall correlated with Draught and 8 other fieldsHigh correlation
Latitude is highly overall correlated with EndLatitude and 6 other fieldsHigh correlation
Length is highly overall correlated with Breadth and 1 other fieldsHigh correlation
Longitude is highly overall correlated with EndLatitude and 6 other fieldsHigh correlation
StartLatitude is highly overall correlated with EndLatitude and 6 other fieldsHigh correlation
StartLongitude is highly overall correlated with EndLatitude and 6 other fieldsHigh correlation
StartPort is highly overall correlated with Draught and 8 other fieldsHigh correlation
TH is highly overall correlated with COGHigh correlation
shiptype is highly overall correlated with EndPort and 1 other fieldsHigh correlation
Draught has 16270 (1.5%) missing values Missing
Length has 10924 (1.0%) zeros Zeros
Breadth has 10924 (1.0%) zeros Zeros

Reproduction

Analysis started2025-06-05 17:23:41.970287
Analysis finished2025-06-05 17:24:49.851740
Duration1 minute and 7.88 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

TripID
Real number (ℝ)

Distinct1126
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1086592.4
Minimum5944
Maximum2278147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.1 MiB
2025-06-05T19:24:49.919112image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum5944
5-th percentile50976
Q1395842
median1111606
Q31660361
95-th percentile2183444
Maximum2278147
Range2272203
Interquartile range (IQR)1264519

Descriptive statistics

Standard deviation687531.89
Coefficient of variation (CV)0.63274133
Kurtosis-1.2954519
Mean1086592.4
Median Absolute Deviation (MAD)590935
Skewness0.073698102
Sum1.1525572 × 1012
Variance4.727001 × 1011
MonotonicityNot monotonic
2025-06-05T19:24:50.020308image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
665635 5901
 
0.6%
1007947 5520
 
0.5%
665728 5073
 
0.5%
665659 4206
 
0.4%
771862 3921
 
0.4%
183671 3870
 
0.4%
665761 3732
 
0.4%
1008001 3729
 
0.4%
5944 3717
 
0.4%
183784 3394
 
0.3%
Other values (1116) 1017645
95.9%
ValueCountFrequency (%)
5944 3717
0.4%
10257 579
 
0.1%
19002 41
 
< 0.1%
19585 1646
0.2%
23834 1241
 
0.1%
24805 595
 
0.1%
25124 659
 
0.1%
28257 1282
 
0.1%
29139 1255
 
0.1%
29152 1305
 
0.1%
ValueCountFrequency (%)
2278147 518
 
< 0.1%
2278140 588
 
0.1%
2278125 528
 
< 0.1%
2278114 1148
0.1%
2278113 1148
0.1%
2278085 1566
0.1%
2278084 1566
0.1%
2278083 1566
0.1%
2278070 1482
0.1%
2278069 1482
0.1%

StartLatitude
Real number (ℝ)

High correlation 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.002772
Minimum53.33
Maximum54.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.1 MiB
2025-06-05T19:24:50.103953image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum53.33
5-th percentile53.52
Q153.58
median54.36
Q354.36
95-th percentile54.36
Maximum54.54
Range1.21
Interquartile range (IQR)0.78

Descriptive statistics

Standard deviation0.39536968
Coefficient of variation (CV)0.0073212849
Kurtosis-1.9299808
Mean54.002772
Median Absolute Deviation (MAD)0
Skewness-0.2043408
Sum57281172
Variance0.15631718
MonotonicityNot monotonic
2025-06-05T19:24:50.186970image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
54.36 571220
53.9%
53.58 222955
 
21.0%
53.59 61008
 
5.8%
53.53 45805
 
4.3%
53.51 34010
 
3.2%
53.57 29724
 
2.8%
53.6 25943
 
2.4%
53.52 17521
 
1.7%
53.61 8363
 
0.8%
53.55 5686
 
0.5%
Other values (18) 38473
 
3.6%
ValueCountFrequency (%)
53.33 3169
 
0.3%
53.34 2535
 
0.2%
53.5 1285
 
0.1%
53.51 34010
3.2%
53.52 17521
 
1.7%
53.53 45805
4.3%
53.54 1289
 
0.1%
53.55 5686
 
0.5%
53.56 5654
 
0.5%
53.57 29724
2.8%
ValueCountFrequency (%)
54.54 3672
 
0.3%
54.49 552
 
0.1%
54.37 1260
 
0.1%
54.36 571220
53.9%
54.33 3168
 
0.3%
54.31 1158
 
0.1%
53.75 976
 
0.1%
53.74 530
 
< 0.1%
53.67 4300
 
0.4%
53.66 411
 
< 0.1%

StartLongitude
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4050804
Minimum8.14
Maximum10.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.1 MiB
2025-06-05T19:24:50.270257image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum8.14
5-th percentile8.5
Q18.52
median10.14
Q310.14
95-th percentile10.14
Maximum10.34
Range2.2
Interquartile range (IQR)1.62

Descriptive statistics

Standard deviation0.81150576
Coefficient of variation (CV)0.086283767
Kurtosis-1.9401815
Mean9.4050804
Median Absolute Deviation (MAD)0
Skewness-0.20234872
Sum9976044.1
Variance0.65854161
MonotonicityNot monotonic
2025-06-05T19:24:50.349905image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
10.14 543986
51.3%
8.52 194761
 
18.4%
8.53 104786
 
9.9%
8.57 60852
 
5.7%
8.51 40838
 
3.9%
10.13 24050
 
2.3%
8.54 15232
 
1.4%
8.5 12688
 
1.2%
8.15 10967
 
1.0%
8.49 9064
 
0.9%
Other values (24) 43484
 
4.1%
ValueCountFrequency (%)
8.14 485
 
< 0.1%
8.15 10967
1.0%
8.16 1298
 
0.1%
8.19 976
 
0.1%
8.22 530
 
< 0.1%
8.36 2156
 
0.2%
8.37 2144
 
0.2%
8.38 411
 
< 0.1%
8.39 984
 
0.1%
8.4 523
 
< 0.1%
ValueCountFrequency (%)
10.34 552
 
0.1%
10.29 3672
 
0.3%
10.18 3188
 
0.3%
10.17 2939
 
0.3%
10.16 1489
 
0.1%
10.15 1154
 
0.1%
10.14 543986
51.3%
10.13 24050
 
2.3%
8.57 60852
 
5.7%
8.56 3146
 
0.3%
Distinct953
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.1 MiB
Minimum2016-01-13 06:03:00
Maximum2017-05-26 19:44:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-05T19:24:50.440210image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:50.539803image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

EndLatitude
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.045262
Minimum53.47
Maximum54.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.1 MiB
2025-06-05T19:24:50.622993image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum53.47
5-th percentile53.5
Q153.53
median54.38
Q354.53
95-th percentile54.54
Maximum54.64
Range1.17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.47881511
Coefficient of variation (CV)0.0088595206
Kurtosis-1.9291169
Mean54.045262
Median Absolute Deviation (MAD)0.16
Skewness-0.15909692
Sum57326241
Variance0.22926391
MonotonicityNot monotonic
2025-06-05T19:24:50.705670image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
53.53 248414
23.4%
54.54 223218
21.0%
53.5 120692
11.4%
54.38 97636
 
9.2%
54.52 56917
 
5.4%
53.52 44887
 
4.2%
54.53 44210
 
4.2%
54.44 42625
 
4.0%
53.54 37792
 
3.6%
54.43 30822
 
2.9%
Other values (19) 113495
10.7%
ValueCountFrequency (%)
53.47 1291
 
0.1%
53.48 677
 
0.1%
53.49 10546
 
1.0%
53.5 120692
11.4%
53.51 10832
 
1.0%
53.52 44887
 
4.2%
53.53 248414
23.4%
53.54 37792
 
3.6%
53.56 678
 
0.1%
53.61 2018
 
0.2%
ValueCountFrequency (%)
54.64 1646
 
0.2%
54.59 740
 
0.1%
54.54 223218
21.0%
54.53 44210
 
4.2%
54.52 56917
 
5.4%
54.51 5176
 
0.5%
54.5 522
 
< 0.1%
54.47 1282
 
0.1%
54.46 7698
 
0.7%
54.45 8806
 
0.8%

EndLongitude
Real number (ℝ)

High correlation 

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.668992
Minimum9.5
Maximum18.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.1 MiB
2025-06-05T19:24:50.922658image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum9.5
5-th percentile9.9
Q19.92
median18.5
Q318.6
95-th percentile18.71
Maximum18.92
Range9.42
Interquartile range (IQR)8.68

Descriptive statistics

Standard deviation4.3208528
Coefficient of variation (CV)0.2945569
Kurtosis-1.9620247
Mean14.668992
Median Absolute Deviation (MAD)0.21
Skewness-0.19134138
Sum15559517
Variance18.669769
MonotonicityNot monotonic
2025-06-05T19:24:51.021752image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
9.91 125320
11.8%
18.51 120016
 
11.3%
18.5 103202
 
9.7%
9.93 88646
 
8.4%
9.9 76211
 
7.2%
9.95 72994
 
6.9%
18.66 49594
 
4.7%
18.65 49227
 
4.6%
18.67 38212
 
3.6%
18.71 35136
 
3.3%
Other values (37) 302150
28.5%
ValueCountFrequency (%)
9.5 1190
 
0.1%
9.51 661
 
0.1%
9.54 1196
 
0.1%
9.55 178
 
< 0.1%
9.56 644
 
0.1%
9.73 678
 
0.1%
9.82 27618
 
2.6%
9.83 6963
 
0.7%
9.88 1222
 
0.1%
9.9 76211
7.2%
ValueCountFrequency (%)
18.92 1646
 
0.2%
18.88 740
 
0.1%
18.83 9886
 
0.9%
18.82 18777
1.8%
18.81 4884
 
0.5%
18.75 1501
 
0.1%
18.72 3778
 
0.4%
18.71 35136
3.3%
18.7 7901
 
0.7%
18.68 3372
 
0.3%
Distinct943
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.1 MiB
Minimum2016-01-13 14:36:00
Maximum2017-05-27 22:11:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-05T19:24:51.118379image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:51.215619image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

StartPort
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.9 MiB
KIEL
581029 
BREMERHAVEN
479678 
GDYNIA
 
1

Length

Max length11
Median length4
Mean length7.1655724
Min length4

Characters and Unicode

Total characters7600580
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBREMERHAVEN
2nd rowBREMERHAVEN
3rd rowBREMERHAVEN
4th rowBREMERHAVEN
5th rowBREMERHAVEN

Common Values

ValueCountFrequency (%)
KIEL 581029
54.8%
BREMERHAVEN 479678
45.2%
GDYNIA 1
 
< 0.1%

Length

2025-06-05T19:24:51.304582image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-05T19:24:51.372115image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
ValueCountFrequency (%)
kiel 581029
54.8%
bremerhaven 479678
45.2%
gdynia 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 2020063
26.6%
R 959356
12.6%
I 581030
 
7.6%
K 581029
 
7.6%
L 581029
 
7.6%
A 479679
 
6.3%
N 479679
 
6.3%
B 479678
 
6.3%
H 479678
 
6.3%
M 479678
 
6.3%
Other values (4) 479681
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7600580
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 2020063
26.6%
R 959356
12.6%
I 581030
 
7.6%
K 581029
 
7.6%
L 581029
 
7.6%
A 479679
 
6.3%
N 479679
 
6.3%
B 479678
 
6.3%
H 479678
 
6.3%
M 479678
 
6.3%
Other values (4) 479681
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7600580
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 2020063
26.6%
R 959356
12.6%
I 581030
 
7.6%
K 581029
 
7.6%
L 581029
 
7.6%
A 479679
 
6.3%
N 479679
 
6.3%
B 479678
 
6.3%
H 479678
 
6.3%
M 479678
 
6.3%
Other values (4) 479681
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7600580
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 2020063
26.6%
R 959356
12.6%
I 581030
 
7.6%
K 581029
 
7.6%
L 581029
 
7.6%
A 479679
 
6.3%
N 479679
 
6.3%
B 479678
 
6.3%
H 479678
 
6.3%
M 479678
 
6.3%
Other values (4) 479681
 
6.3%

EndPort
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.2 MiB
GDYNIA
581029 
HAMBURG
479678 
486589203
 
1

Length

Max length9
Median length6
Mean length6.4522272
Min length6

Characters and Unicode

Total characters6843929
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowHAMBURG
2nd rowHAMBURG
3rd rowHAMBURG
4th rowHAMBURG
5th rowHAMBURG

Common Values

ValueCountFrequency (%)
GDYNIA 581029
54.8%
HAMBURG 479678
45.2%
486589203 1
 
< 0.1%

Length

2025-06-05T19:24:51.438572image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-05T19:24:51.494334image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
ValueCountFrequency (%)
gdynia 581029
54.8%
hamburg 479678
45.2%
486589203 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
G 1060707
15.5%
A 1060707
15.5%
D 581029
8.5%
Y 581029
8.5%
N 581029
8.5%
I 581029
8.5%
H 479678
7.0%
M 479678
7.0%
B 479678
7.0%
U 479678
7.0%
Other values (9) 479687
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6843929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 1060707
15.5%
A 1060707
15.5%
D 581029
8.5%
Y 581029
8.5%
N 581029
8.5%
I 581029
8.5%
H 479678
7.0%
M 479678
7.0%
B 479678
7.0%
U 479678
7.0%
Other values (9) 479687
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6843929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 1060707
15.5%
A 1060707
15.5%
D 581029
8.5%
Y 581029
8.5%
N 581029
8.5%
I 581029
8.5%
H 479678
7.0%
M 479678
7.0%
B 479678
7.0%
U 479678
7.0%
Other values (9) 479687
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6843929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 1060707
15.5%
A 1060707
15.5%
D 581029
8.5%
Y 581029
8.5%
N 581029
8.5%
I 581029
8.5%
H 479678
7.0%
M 479678
7.0%
B 479678
7.0%
U 479678
7.0%
Other values (9) 479687
7.0%

time
Date

Distinct414192
Distinct (%)39.0%
Missing1
Missing (%)< 0.1%
Memory size8.1 MiB
Minimum2016-01-13 07:03:00+01:00
Maximum2017-05-28 00:11:00+02:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-05T19:24:51.568379image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:51.664708image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

shiptype
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.004988
Minimum0
Maximum155
Zeros7224
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size8.1 MiB
2025-06-05T19:24:51.736844image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile70
Q170
median71
Q371
95-th percentile81
Maximum155
Range155
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.036471
Coefficient of variation (CV)0.097721994
Kurtosis72.034492
Mean72.004988
Median Absolute Deviation (MAD)1
Skewness-7.0217005
Sum76376267
Variance49.511924
MonotonicityNot monotonic
2025-06-05T19:24:51.801848image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
71 456256
43.0%
70 355276
33.5%
79 142274
 
13.4%
81 65116
 
6.1%
72 13891
 
1.3%
80 11569
 
1.1%
0 7224
 
0.7%
74 3745
 
0.4%
73 3627
 
0.3%
69 1144
 
0.1%
Other values (2) 586
 
0.1%
ValueCountFrequency (%)
0 7224
 
0.7%
69 1144
 
0.1%
70 355276
33.5%
71 456256
43.0%
72 13891
 
1.3%
73 3627
 
0.3%
74 3745
 
0.4%
79 142274
 
13.4%
80 11569
 
1.1%
81 65116
 
6.1%
ValueCountFrequency (%)
155 1
 
< 0.1%
89 585
 
0.1%
81 65116
 
6.1%
80 11569
 
1.1%
79 142274
 
13.4%
74 3745
 
0.4%
73 3627
 
0.3%
72 13891
 
1.3%
71 456256
43.0%
70 355276
33.5%

Length
Real number (ℝ)

High correlation  Zeros 

Distinct108
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.19246
Minimum0
Maximum745
Zeros10924
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size8.1 MiB
2025-06-05T19:24:51.887908image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile68
Q188
median134
Q3151
95-th percentile170
Maximum745
Range745
Interquartile range (IQR)63

Descriptive statistics

Standard deviation46.100907
Coefficient of variation (CV)0.3682403
Kurtosis19.6454
Mean125.19246
Median Absolute Deviation (MAD)32
Skewness2.2124777
Sum1.3279264 × 108
Variance2125.2936
MonotonicityNot monotonic
2025-06-05T19:24:51.988719image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151 115425
 
10.9%
134 82214
 
7.8%
125 68105
 
6.4%
88 63797
 
6.0%
68 62288
 
5.9%
155 59903
 
5.6%
82 48473
 
4.6%
168 41015
 
3.9%
101 39221
 
3.7%
79 34122
 
3.2%
Other values (98) 446145
42.1%
ValueCountFrequency (%)
0 10924
 
1.0%
25 1
 
< 0.1%
45 1760
 
0.2%
64 250
 
< 0.1%
65 1362
 
0.1%
66 1509
 
0.1%
68 62288
5.9%
70 6114
 
0.6%
74 9904
 
0.9%
75 10082
 
1.0%
ValueCountFrequency (%)
745 73
 
< 0.1%
666 163
 
< 0.1%
665 1
 
< 0.1%
663 593
 
0.1%
457 19
 
< 0.1%
399 1716
0.2%
397 1153
0.1%
396 660
 
0.1%
377 13
 
< 0.1%
300 1142
0.1%

Breadth
Real number (ℝ)

High correlation  Zeros 

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.793313
Minimum0
Maximum60
Zeros10924
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size8.1 MiB
2025-06-05T19:24:52.080427image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q113
median22
Q324
95-th percentile27
Maximum60
Range60
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.3733717
Coefficient of variation (CV)0.32199621
Kurtosis4.5083163
Mean19.793313
Median Absolute Deviation (MAD)3
Skewness0.5233135
Sum20994925
Variance40.619867
MonotonicityNot monotonic
2025-06-05T19:24:52.166392image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
22 156866
14.8%
12 141817
13.4%
23 141407
13.3%
24 112365
10.6%
13 96755
9.1%
18 70665
6.7%
25 68273
6.4%
26 50806
 
4.8%
19 38464
 
3.6%
11 35475
 
3.3%
Other values (27) 147815
13.9%
ValueCountFrequency (%)
0 10924
 
1.0%
8 1760
 
0.2%
9 637
 
0.1%
9.3 1
 
< 0.1%
10 3753
 
0.4%
11 35475
 
3.3%
12 141817
13.4%
13 96755
9.1%
14 13143
 
1.2%
15 14038
 
1.3%
ValueCountFrequency (%)
60 1716
0.2%
59 660
 
0.1%
56 1153
 
0.1%
48 1142
 
0.1%
45 85
 
< 0.1%
42 1071
 
0.1%
40 3919
0.4%
36 652
 
0.1%
35 564
 
0.1%
33 681
 
0.1%

Draught
Real number (ℝ)

High correlation  Missing 

Distinct239
Distinct (%)< 0.1%
Missing16270
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean6.3532147
Minimum0
Maximum54.61
Zeros50
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.1 MiB
2025-06-05T19:24:52.255383image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.8
Q14.3
median6.99
Q38.1
95-th percentile9.3
Maximum54.61
Range54.61
Interquartile range (IQR)3.8

Descriptive statistics

Standard deviation2.1822369
Coefficient of variation (CV)0.34348546
Kurtosis-0.86733974
Mean6.3532147
Median Absolute Deviation (MAD)1.61
Skewness-0.23438602
Sum6635538.9
Variance4.7621577
MonotonicityNot monotonic
2025-06-05T19:24:52.349573image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.2 62267
 
5.9%
2.8 56246
 
5.3%
8.3 30569
 
2.9%
7.9 21869
 
2.1%
8.1 20690
 
2.0%
7.7 20220
 
1.9%
7.8 20045
 
1.9%
8.4 19158
 
1.8%
3.8 16693
 
1.6%
8 14619
 
1.4%
Other values (229) 762062
71.8%
(Missing) 16270
 
1.5%
ValueCountFrequency (%)
0 50
 
< 0.1%
0.1 637
 
0.1%
1.93 1771
 
0.2%
2.3 1612
 
0.2%
2.4 3660
 
0.3%
2.5 1373
 
0.1%
2.6 7279
 
0.7%
2.63 41
 
< 0.1%
2.7 1783
 
0.2%
2.8 56246
5.3%
ValueCountFrequency (%)
54.61 1
 
< 0.1%
14 15
 
< 0.1%
13.23 544
0.1%
11.7 645
0.1%
11.54 1071
0.1%
11.45 551
0.1%
11.4 1223
0.1%
11.24 691
0.1%
11.1 627
0.1%
11 368
 
< 0.1%

Latitude
Real number (ℝ)

High correlation 

Distinct274
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.254708
Minimum14.63
Maximum56.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.1 MiB
2025-06-05T19:24:52.557066image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum14.63
5-th percentile53.56
Q153.84
median54.42
Q354.64
95-th percentile54.89
Maximum56.34
Range41.71
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.4627239
Coefficient of variation (CV)0.0085287327
Kurtosis49.525461
Mean54.254708
Median Absolute Deviation (MAD)0.44
Skewness-0.6150148
Sum57548403
Variance0.21411341
MonotonicityNot monotonic
2025-06-05T19:24:52.651948image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.87 36524
 
3.4%
53.84 25419
 
2.4%
53.98 24948
 
2.4%
54.53 24948
 
2.4%
54.41 21658
 
2.0%
53.54 21377
 
2.0%
53.86 21117
 
2.0%
53.85 20259
 
1.9%
53.83 20080
 
1.9%
53.97 19608
 
1.8%
Other values (264) 824770
77.8%
ValueCountFrequency (%)
14.63 1
 
< 0.1%
53.33 13
 
< 0.1%
53.34 40
< 0.1%
53.35 29
< 0.1%
53.36 26
< 0.1%
53.37 25
< 0.1%
53.38 27
< 0.1%
53.39 26
< 0.1%
53.4 23
< 0.1%
53.41 24
< 0.1%
ValueCountFrequency (%)
56.34 2
 
< 0.1%
56.33 4
 
< 0.1%
56.32 4
 
< 0.1%
56.31 3
 
< 0.1%
56.3 3
 
< 0.1%
56.29 12
< 0.1%
56.28 8
< 0.1%
56.27 5
< 0.1%
56.26 5
< 0.1%
56.25 6
< 0.1%

Longitude
Real number (ℝ)

High correlation 

Distinct1285
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.79557
Minimum2.32
Maximum20.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.1 MiB
2025-06-05T19:24:52.746088image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum2.32
5-th percentile8.07
Q18.84
median10.63
Q313.91
95-th percentile18.78
Maximum20.66
Range18.34
Interquartile range (IQR)5.07

Descriptive statistics

Standard deviation3.4893558
Coefficient of variation (CV)0.29581916
Kurtosis-0.52728493
Mean11.79557
Median Absolute Deviation (MAD)2.16
Skewness0.83164662
Sum12511656
Variance12.175604
MonotonicityNot monotonic
2025-06-05T19:24:52.845550image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.51 5970
 
0.6%
8.52 5919
 
0.6%
8.12 5858
 
0.6%
8.11 5439
 
0.5%
9.93 5159
 
0.5%
9.9 5087
 
0.5%
9.5 5032
 
0.5%
8.51 4573
 
0.4%
9.39 4068
 
0.4%
9.38 4061
 
0.4%
Other values (1275) 1009542
95.2%
ValueCountFrequency (%)
2.32 1
 
< 0.1%
7.71 2
 
< 0.1%
7.72 45
< 0.1%
7.73 102
< 0.1%
7.74 99
< 0.1%
7.75 108
< 0.1%
7.76 83
< 0.1%
7.77 83
< 0.1%
7.78 81
< 0.1%
7.79 78
< 0.1%
ValueCountFrequency (%)
20.66 1
 
< 0.1%
20.65 3
< 0.1%
20.64 2
 
< 0.1%
20.63 2
 
< 0.1%
20.62 6
< 0.1%
20.61 6
< 0.1%
20.6 4
< 0.1%
20.59 6
< 0.1%
20.58 6
< 0.1%
20.57 6
< 0.1%

SOG
Real number (ℝ)

Distinct228
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.052629
Minimum0.2
Maximum80.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.1 MiB
2025-06-05T19:24:52.944275image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile6.6
Q19.9
median12
Q314.5
95-th percentile17.4
Maximum80.7
Range80.5
Interquartile range (IQR)4.6

Descriptive statistics

Standard deviation3.4921684
Coefficient of variation (CV)0.2897433
Kurtosis1.0226024
Mean12.052629
Median Absolute Deviation (MAD)2.3
Skewness-0.44726681
Sum12784320
Variance12.19524
MonotonicityNot monotonic
2025-06-05T19:24:53.047205image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.5 14605
 
1.4%
10.4 14338
 
1.4%
10.7 14081
 
1.3%
10.6 14073
 
1.3%
11.7 13702
 
1.3%
10.3 13450
 
1.3%
11.6 13441
 
1.3%
11 13198
 
1.2%
10.9 12878
 
1.2%
11.4 12670
 
1.2%
Other values (218) 924272
87.1%
ValueCountFrequency (%)
0.2 443
 
< 0.1%
0.3 1912
0.2%
0.4 1615
0.2%
0.5 1232
0.1%
0.6 1093
0.1%
0.7 967
0.1%
0.8 1095
0.1%
0.9 1013
0.1%
1 915
0.1%
1.1 874
0.1%
ValueCountFrequency (%)
80.7 1
 
< 0.1%
73.6 2
 
< 0.1%
51.2 1
 
< 0.1%
22.6 1
 
< 0.1%
22.5 1
 
< 0.1%
22.4 2
 
< 0.1%
22.3 1
 
< 0.1%
22.2 4
< 0.1%
22.1 4
< 0.1%
22 6
< 0.1%

COG
Real number (ℝ)

High correlation 

Distinct3602
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.98212
Minimum0
Maximum378
Zeros210
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.1 MiB
2025-06-05T19:24:53.144452image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile45.9
Q182
median100
Q3136.3
95-th percentile314.9
Maximum378
Range378
Interquartile range (IQR)54.3

Descriptive statistics

Standard deviation77.95504
Coefficient of variation (CV)0.61390564
Kurtosis1.1463034
Mean126.98212
Median Absolute Deviation (MAD)22.7
Skewness1.4540593
Sum1.3469095 × 108
Variance6076.9882
MonotonicityNot monotonic
2025-06-05T19:24:53.246622image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 8874
 
0.8%
81 8420
 
0.8%
82 7569
 
0.7%
89 6461
 
0.6%
86 6142
 
0.6%
100 6036
 
0.6%
85 5864
 
0.6%
88 5757
 
0.5%
83 5702
 
0.5%
87 5569
 
0.5%
Other values (3592) 994314
93.7%
ValueCountFrequency (%)
0 210
 
< 0.1%
0.1 580
0.1%
0.2 32
 
< 0.1%
0.3 26
 
< 0.1%
0.4 19
 
< 0.1%
0.5 37
 
< 0.1%
0.6 37
 
< 0.1%
0.7 35
 
< 0.1%
0.8 37
 
< 0.1%
0.9 60
 
< 0.1%
ValueCountFrequency (%)
378 1
 
< 0.1%
360 12
 
< 0.1%
359.9 69
< 0.1%
359.8 41
< 0.1%
359.7 18
 
< 0.1%
359.6 30
< 0.1%
359.5 34
< 0.1%
359.4 24
 
< 0.1%
359.3 41
< 0.1%
359.2 22
 
< 0.1%

TH
Real number (ℝ)

High correlation 

Distinct361
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean131.96792
Minimum0
Maximum511
Zeros623
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.1 MiB
2025-06-05T19:24:53.349003image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47
Q182
median100
Q3140
95-th percentile320
Maximum511
Range511
Interquartile range (IQR)58

Descriptive statistics

Standard deviation88.949001
Coefficient of variation (CV)0.67401988
Kurtosis3.3333632
Mean131.96792
Median Absolute Deviation (MAD)23
Skewness1.8210265
Sum1.399793 × 108
Variance7911.9248
MonotonicityNot monotonic
2025-06-05T19:24:53.448417image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82 25863
 
2.4%
81 24895
 
2.3%
90 23223
 
2.2%
83 23083
 
2.2%
85 20672
 
1.9%
84 20368
 
1.9%
89 18944
 
1.8%
88 17458
 
1.6%
86 17427
 
1.6%
80 17292
 
1.6%
Other values (351) 851482
80.3%
ValueCountFrequency (%)
0 623
0.1%
1 556
0.1%
2 479
< 0.1%
3 531
0.1%
4 319
< 0.1%
5 328
< 0.1%
6 282
< 0.1%
7 330
< 0.1%
8 304
< 0.1%
9 229
 
< 0.1%
ValueCountFrequency (%)
511 12985
1.2%
359 515
 
< 0.1%
358 617
 
0.1%
357 398
 
< 0.1%
356 506
 
< 0.1%
355 594
 
0.1%
354 524
 
< 0.1%
353 331
 
< 0.1%
352 312
 
< 0.1%
351 259
 
< 0.1%
Distinct140
Distinct (%)< 0.1%
Missing5898
Missing (%)0.6%
Memory size64.9 MiB
2025-06-05T19:24:53.584023image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Length

Max length21
Median length20
Mean length7.3506698
Min length2

Characters and Unicode

Total characters7753560
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowHAMBURG
2nd rowHAMBURG
3rd rowHAMBURG
4th rowHAMBURG
5th rowHAMBURG
ValueCountFrequency (%)
hamburg 272461
25.8%
gdynia 222144
21.1%
gdansk 159967
15.2%
deham 103184
 
9.8%
gdynia.via.nok 35601
 
3.4%
de.ham 34098
 
3.2%
plgdn 29926
 
2.8%
finkenwerder 27420
 
2.6%
gdansk.via.nok 25973
 
2.5%
debrv.>.deham 21493
 
2.0%
Other values (120) 122543
11.6%
2025-06-05T19:24:53.793227image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1077309
13.9%
G 826091
10.7%
D 808213
10.4%
N 683408
 
8.8%
H 460718
 
5.9%
M 451050
 
5.8%
I 427304
 
5.5%
R 368528
 
4.8%
K 350212
 
4.5%
E 327581
 
4.2%
Other values (36) 1973146
25.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7753560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1077309
13.9%
G 826091
10.7%
D 808213
10.4%
N 683408
 
8.8%
H 460718
 
5.9%
M 451050
 
5.8%
I 427304
 
5.5%
R 368528
 
4.8%
K 350212
 
4.5%
E 327581
 
4.2%
Other values (36) 1973146
25.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7753560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1077309
13.9%
G 826091
10.7%
D 808213
10.4%
N 683408
 
8.8%
H 460718
 
5.9%
M 451050
 
5.8%
I 427304
 
5.5%
R 368528
 
4.8%
K 350212
 
4.5%
E 327581
 
4.2%
Other values (36) 1973146
25.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7753560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1077309
13.9%
G 826091
10.7%
D 808213
10.4%
N 683408
 
8.8%
H 460718
 
5.9%
M 451050
 
5.8%
I 427304
 
5.5%
R 368528
 
4.8%
K 350212
 
4.5%
E 327581
 
4.2%
Other values (36) 1973146
25.4%
Distinct223
Distinct (%)< 0.1%
Missing9891
Missing (%)0.9%
Memory size69.4 MiB
2025-06-05T19:24:53.904347image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Length

Max length31
Median length28
Mean length11.994006
Min length2

Characters and Unicode

Total characters12603505
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowDAIS1.81b.90b.71.71a
2nd rowDAIS1.81b.90b.71.71a
3rd rowDAIS1.81b.90b.71.71a
4th rowDAIS1.81b.90b.71.71a
5th rowDAIS1.81b.90b.71.71a
ValueCountFrequency (%)
h7001 158252
15.1%
51.71a.h7001.81b 99047
 
9.4%
71a.h7001.81b 85871
 
8.2%
71.51.71a.h7001.81b 67024
 
6.4%
71.71a.h7001.81b 61410
 
5.8%
71a.h7001 48129
 
4.6%
71a.h7001.67b 42889
 
4.1%
71.71a.h7001 40625
 
3.9%
71.71a.h7001.67b 33854
 
3.2%
71.71a.67b 33694
 
3.2%
Other values (213) 380022
36.2%
2025-06-05T19:24:54.108778image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2823931
22.4%
7 2263856
18.0%
. 2084889
16.5%
0 1722274
13.7%
H 838755
 
6.7%
a 829978
 
6.6%
b 682836
 
5.4%
8 439536
 
3.5%
5 287020
 
2.3%
6 226348
 
1.8%
Other values (20) 404082
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12603505
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2823931
22.4%
7 2263856
18.0%
. 2084889
16.5%
0 1722274
13.7%
H 838755
 
6.7%
a 829978
 
6.6%
b 682836
 
5.4%
8 439536
 
3.5%
5 287020
 
2.3%
6 226348
 
1.8%
Other values (20) 404082
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12603505
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2823931
22.4%
7 2263856
18.0%
. 2084889
16.5%
0 1722274
13.7%
H 838755
 
6.7%
a 829978
 
6.6%
b 682836
 
5.4%
8 439536
 
3.5%
5 287020
 
2.3%
6 226348
 
1.8%
Other values (20) 404082
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12603505
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2823931
22.4%
7 2263856
18.0%
. 2084889
16.5%
0 1722274
13.7%
H 838755
 
6.7%
a 829978
 
6.6%
b 682836
 
5.4%
8 439536
 
3.5%
5 287020
 
2.3%
6 226348
 
1.8%
Other values (20) 404082
 
3.2%

Interactions

2025-06-05T19:24:42.665469image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:12.771507image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:15.013505image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:17.326621image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:19.664800image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:22.041290image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:24.490426image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:26.718835image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:29.039919image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:31.233370image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:33.387406image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:35.627708image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:37.939724image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:40.356383image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:42.822825image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:12.917557image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:15.158322image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:17.516697image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:19.839824image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:22.202029image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:24.646747image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:26.876407image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:29.193279image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:31.385657image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:33.539742image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:35.787992image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:38.105430image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:40.515166image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:42.985498image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:13.066243image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:15.311239image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:17.673378image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:20.005288image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:22.363939image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:24.807117image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:27.030496image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:29.350239image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:31.541866image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:33.696694image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:35.957726image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:38.274991image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:40.685235image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:43.141043image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:13.210389image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:15.467965image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:17.816018image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:20.155508image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:22.535762image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:24.962199image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:27.188027image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:29.500914image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:31.691616image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:33.952905image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:36.117623image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:38.438448image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:40.844661image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:43.300838image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:13.366229image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:15.629469image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:17.976366image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:20.309633image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:22.691386image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:25.121976image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:27.339638image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:29.656012image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:31.844599image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:34.105277image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:36.283850image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:38.609067image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:41.010491image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:43.456564image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:13.512469image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:15.784911image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:18.126979image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:20.471878image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:22.866242image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:25.275320image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:27.498791image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:29.806478image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:31.996458image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:34.249785image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:36.444167image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:38.768390image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:41.168322image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:43.617596image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:13.673008image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:15.948334image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:18.310359image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:20.648878image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:23.026990image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:25.432532image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:27.659337image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:29.965966image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:32.153306image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:34.403126image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:36.616107image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:38.928612image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:41.329504image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:43.777439image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:13.830619image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:16.111319image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:18.509751image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:20.818920image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:23.212766image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:25.597118image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:27.810480image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:30.118975image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:32.309063image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:34.558604image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:36.782754image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:39.094913image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:41.504655image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:43.938213image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:14.003236image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:16.278001image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:18.677661image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:20.996203image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:23.383627image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:25.767256image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:27.972462image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:30.275904image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:32.456894image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:34.717913image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:36.955564image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:39.379225image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:41.676042image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:44.102622image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:14.187087image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:16.436956image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:18.835472image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:21.176494image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:23.563336image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:25.923079image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:28.132795image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:30.429474image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:32.610174image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:34.858751image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:37.119472image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:39.544596image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
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2025-06-05T19:24:28.893841image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:31.078620image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:33.238855image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:35.478453image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:37.786074image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:40.205137image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-05T19:24:42.497286image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Correlations

2025-06-05T19:24:54.184859image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
BreadthCOGDraughtEndLatitudeEndLongitudeEndPortLatitudeLengthLongitudeSOGStartLatitudeStartLongitudeStartPortTHTripIDshiptype
Breadth1.0000.0130.8890.114-0.2140.171-0.0440.942-0.0500.4100.044-0.1540.1710.002-0.0190.268
COG0.0131.000-0.020-0.299-0.2980.311-0.370-0.026-0.1540.010-0.328-0.3110.3110.9410.0070.046
Draught0.889-0.0201.0000.165-0.1100.7110.0400.9000.0310.4130.155-0.0680.711-0.0240.0130.274
EndLatitude0.114-0.2990.1651.0000.5600.7070.7640.1890.751-0.0450.8210.7960.707-0.290-0.013-0.136
EndLongitude-0.214-0.298-0.1100.5601.0001.0000.737-0.1030.747-0.2630.8040.8331.000-0.2730.015-0.017
EndPort0.1710.3110.7110.7071.0001.0001.0000.1840.6500.4110.7070.7071.0000.3970.2370.709
Latitude-0.044-0.3700.0400.7640.7371.0001.0000.0490.781-0.0540.8140.8011.000-0.348-0.012-0.096
Length0.942-0.0260.9000.189-0.1030.1840.0491.0000.0460.3970.181-0.0720.184-0.033-0.0460.223
Longitude-0.050-0.1540.0310.7510.7470.6500.7810.0461.000-0.2230.8150.8020.650-0.141-0.011-0.099
SOG0.4100.0100.413-0.045-0.2630.411-0.0540.397-0.2231.000-0.115-0.2050.411-0.012-0.0610.163
StartLatitude0.044-0.3280.1550.8210.8040.7070.8140.1810.815-0.1151.0000.8120.707-0.309-0.019-0.115
StartLongitude-0.154-0.311-0.0680.7960.8330.7070.801-0.0720.802-0.2050.8121.0000.707-0.2920.005-0.074
StartPort0.1710.3110.7110.7071.0001.0001.0000.1840.6500.4110.7070.7071.0000.3970.2370.709
TH0.0020.941-0.024-0.290-0.2730.397-0.348-0.033-0.141-0.012-0.309-0.2920.3971.0000.0080.046
TripID-0.0190.0070.013-0.0130.0150.237-0.012-0.046-0.011-0.061-0.0190.0050.2370.0081.0000.195
shiptype0.2680.0460.274-0.136-0.0170.709-0.0960.223-0.0990.163-0.115-0.0740.7090.0460.1951.000

Missing values

2025-06-05T19:24:45.164578image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-05T19:24:46.575663image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-05T19:24:48.862613image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

TripIDStartLatitudeStartLongitudeStartTimeEndLatitudeEndLongitudeEndTimeStartPortEndPorttimeshiptypeLengthBreadthDraughtLatitudeLongitudeSOGCOGTHDestinationAisSourcen
03913153.578.53'2016-01-24 08:06'53.539.9'2016-01-24 16:44'BREMERHAVENHAMBURG2016-01-24 09:07:00+01:007127742.011.5453.578.530.7331.2143.0HAMBURGDAIS1.81b.90b.71.71a
13913153.578.53'2016-01-24 08:06'53.539.9'2016-01-24 16:44'BREMERHAVENHAMBURG2016-01-24 09:10:00+01:007127742.011.5453.578.531.6315.3117.0HAMBURGDAIS1.81b.90b.71.71a
23913153.578.53'2016-01-24 08:06'53.539.9'2016-01-24 16:44'BREMERHAVENHAMBURG2016-01-24 09:10:00+01:007127742.011.5453.578.532.8322.6100.0HAMBURGDAIS1.81b.90b.71.71a
33913153.578.53'2016-01-24 08:06'53.539.9'2016-01-24 16:44'BREMERHAVENHAMBURG2016-01-24 09:12:00+01:007127742.011.5453.578.532.8286.374.0HAMBURGDAIS1.81b.90b.71.71a
43913153.578.53'2016-01-24 08:06'53.539.9'2016-01-24 16:44'BREMERHAVENHAMBURG2016-01-24 09:16:00+01:007127742.011.5453.578.534.3333.1333.0HAMBURGDAIS1.81b.90b.71.71a
53913153.578.53'2016-01-24 08:06'53.539.9'2016-01-24 16:44'BREMERHAVENHAMBURG2016-01-24 09:17:00+01:007127742.011.5453.578.535.2334.0333.0HAMBURG51.DAIS1.81b.90b.71.71a
63913153.578.53'2016-01-24 08:06'53.539.9'2016-01-24 16:44'BREMERHAVENHAMBURG2016-01-24 09:18:00+01:007127742.011.5453.578.535.7333.0333.0HAMBURG51.DAIS1.81b.90b.71.71a
73913153.578.53'2016-01-24 08:06'53.539.9'2016-01-24 16:44'BREMERHAVENHAMBURG2016-01-24 09:19:00+01:007127742.011.5453.578.526.3333.0333.0HAMBURG51.DAIS1.81b.90b.71.71a
83913153.578.53'2016-01-24 08:06'53.539.9'2016-01-24 16:44'BREMERHAVENHAMBURG2016-01-24 09:20:00+01:007127742.011.5453.588.526.8333.0333.0HAMBURG51.DAIS1.81b.90b.71.71a
93913153.578.53'2016-01-24 08:06'53.539.9'2016-01-24 16:44'BREMERHAVENHAMBURG2016-01-24 09:21:00+01:007127742.011.5453.588.527.1332.1333.0HAMBURG51.DAIS1.81b.90b.71.71a
TripIDStartLatitudeStartLongitudeStartTimeEndLatitudeEndLongitudeEndTimeStartPortEndPorttimeshiptypeLengthBreadthDraughtLatitudeLongitudeSOGCOGTHDestinationAisSourcen
1060698220404954.3610.14'2017-04-03 07:54'54.3818.66'2017-04-04 15:28'KIELGDYNIA2017-04-04 16:02:00+02:00708913.04.054.5018.747.2222.0215.0GDANSKH7001
1060699220404954.3610.14'2017-04-03 07:54'54.3818.66'2017-04-04 15:28'KIELGDYNIA2017-04-04 16:01:00+02:00708913.04.054.5018.747.2221.6215.0GDANSKH7001
1060700220404954.3610.14'2017-04-03 07:54'54.3818.66'2017-04-04 15:28'KIELGDYNIA2017-04-04 16:00:00+02:00708913.04.054.5018.747.2221.5215.0GDANSKH7001
1060701220404954.3610.14'2017-04-03 07:54'54.3818.66'2017-04-04 15:28'KIELGDYNIA2017-04-04 15:59:00+02:00708913.04.054.5018.757.2221.2215.0GDANSKH7001
1060702220404954.3610.14'2017-04-03 07:54'54.3818.66'2017-04-04 15:28'KIELGDYNIA2017-04-04 15:58:00+02:00708913.04.054.5018.757.2220.8215.0GDANSKH7001
1060703220404954.3610.14'2017-04-03 07:54'54.3818.66'2017-04-04 15:28'KIELGDYNIA2017-04-04 15:57:00+02:00708913.04.054.5118.757.2221.0215.0GDANSKH7001
1060704220404954.3610.14'2017-04-03 07:54'54.3818.66'2017-04-04 15:28'KIELGDYNIA2017-04-04 15:56:00+02:00708913.04.054.5118.757.2221.9215.0GDANSKH7001
1060705220404954.3610.14'2017-04-03 07:54'54.3818.66'2017-04-04 15:28'KIELGDYNIA2017-04-04 15:55:00+02:00708913.04.054.5118.757.2222.1215.0GDANSKH7001
1060706220404954.3610.14'2017-04-03 07:54'54.3818.66'2017-04-04 15:28'KIELGDYNIA2017-04-04 15:54:00+02:00708913.04.054.5118.767.2221.2215.0GDANSKH7001
1060707220404954.3610.14'2017-04-03 07:54'54.3818.66'2017-04-04 15:28'KIELGDYNIA2017-04-04 15:53:00+02:00708913.04.054.5118.767.2221.0214.0GDANSKH7001

Duplicate rows

Most frequently occurring

TripIDStartLatitudeStartLongitudeStartTimeEndLatitudeEndLongitudeEndTimeStartPortEndPorttimeshiptypeLengthBreadthDraughtLatitudeLongitudeSOGCOGTHDestinationAisSourcen# duplicates
457594454.3610.14'2016-08-08 04:10'54.5218.52'2016-08-09 12:31'KIELGDYNIA2016-08-08 13:48:00+02:00708212.05.354.4712.2411.429.527.0GDYNIA.VIA.NOK71a.H70016
681594454.3610.14'2016-08-08 04:10'54.5218.52'2016-08-09 12:31'KIELGDYNIA2016-08-08 17:33:00+02:00708212.05.354.6913.3110.874.475.0GDYNIA.VIA.NOK71a6
1914431280253.588.53'2016-07-15 19:02'53.529.92'2016-07-16 04:45'BREMERHAVENHAMBURG2016-07-16 00:31:00+02:007112522.07.053.998.1810.390.091.0HAMBURG71a.DAIS3.H7001.81b6
1972331280353.588.53'2016-07-15 19:02'53.529.92'2016-07-16 04:45'BREMERHAVENHAMBURG2016-07-16 00:31:00+02:007112522.07.053.998.1810.390.091.0HAMBURG71a.DAIS3.H7001.81b6
2030231280453.588.53'2016-07-15 19:02'53.529.92'2016-07-16 04:45'BREMERHAVENHAMBURG2016-07-16 00:31:00+02:007112522.07.053.998.1810.390.091.0HAMBURG71a.DAIS3.H7001.81b6
2413931288953.588.52'2016-08-05 13:53'53.539.91'2016-08-05 22:23'BREMERHAVENHAMBURG2016-08-05 23:43:00+02:007112522.06.753.559.7912.8107.0107.0HAMBURG71a.H7001.81b6
2464931289053.588.52'2016-08-05 13:53'53.539.91'2016-08-05 22:23'BREMERHAVENHAMBURG2016-08-05 23:43:00+02:007112522.06.753.559.7912.8107.0107.0HAMBURG71a.H7001.81b6
2515931289153.588.52'2016-08-05 13:53'53.539.91'2016-08-05 22:23'BREMERHAVENHAMBURG2016-08-05 23:43:00+02:007112522.06.753.559.7912.8107.0107.0HAMBURG71a.H7001.81b6
2557831294653.588.52'2016-08-19 14:55'53.539.91'2016-08-20 00:10'BREMERHAVENHAMBURG2016-08-19 23:15:00+02:007112522.06.453.869.2712.5115.0116.0DEHAM51.71a.H7001.81b6
2612531294753.588.52'2016-08-19 14:55'53.539.91'2016-08-20 00:10'BREMERHAVENHAMBURG2016-08-19 23:15:00+02:007112522.06.453.869.2712.5115.0116.0DEHAM51.71a.H7001.81b6